Sentiment analysis of sentences used in Indian local languages using Transfer Learning
BERT; GitHub; Sentiment analysis; Transfer learning methods
The growing number of online communication channels has led to a dramatic increase in the volume of user-generated content across all media. Sentiment analysis is given more force by having access to these varying perspectives and feelings. Due to a lack of standardised labelled data, sentiment analysis becomes much more difficult. The goal of sentiment analysis is to determine, from a text sample, an individual's likely emotional reaction to a given event or point of view. When estimating the tone of a text, polarity analysis is commonly used. Negative, Positive and neutral are the labels used by sentiment classifiers. This chapter provides a framework for effective sentiment analysis of sentences from four local language datasets, including Hindi, English, Bangla, and Marathi, using transfer learning. The results show that the BERT model achieves a high level of accuracy when evaluated using random forests, nae Bayes, and decision trees. 2026
Yadav, Priyanka; Yadav, Manoj; Bokan, Reena; Poonia, Ramesh Chandra
Advances in Computers;Volume;143;pp.463-481
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/bs.adcom.2026.01.012 https://www.scopus.com/pages/publications/105030083934?origin=resultslist
Yadav P., Guru Jambheshwar University of Science & Technology, Haryana, Hisar, India; Yadav M., Guru Jambheshwar University of Science & Technology, Haryana, Hisar, India; Bokan R., Gurugram University, Haryana, Gurugram, India; Poonia R.C., Department of Computer Science, CHRIST University, Bangalore, India